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Cited by 122Functional Brain Connectivity StudiesDementia and Cognitive Impairment ResearchAdvanced Neuroimaging Techniques and Applications

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Disrupted Functional Brain Connectivity and Its Association to Structural Connectivity in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

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Sun, Yu, Yin, Qihua, Fang, Rong, Yan, Xiaoxiao, Wang, Ying, Bezerianos, Anastasios, Tang, Huidong, Miao, Fei, Sun, Junfeng·2014·PLoS ONE
DOI 10.1371/journal.pone.0096505PMC PMC4013022
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Abstract

Although anomalies in the topological architecture of whole-brain connectivity have been found to be associated with Alzheimer's disease (AD), our understanding about the progression of AD in a functional connectivity (FC) perspective is still rudimentary and few study has explored the function-structure relations in brain networks of AD patients. By using resting-state functional MRI (fMRI), this study firstly investigated organizational alternations in FC networks in 12 AD patients, 15 amnestic mild cognitive impairment (aMCI) patients, and 14 age-matched healthy aging subjects and found that all three groups exhibit economical small-world network properties. Nonetheless, we found a decline of the optimal architecture in the progression of AD, represented by a more localized modular organization with less efficient local information transfer. Our results also show that aMCI forms a boundary between normal aging and AD and represents a functional continuum between healthy aging and the earliest signs of dementia. Moreover, we revealed a dissociated relationship between the overall FC and structural connectivity (SC) in AD patients. In this study, diffusion tensor imaging tractography was used to map the structural network of the same individuals. The decreased FC-SC coupling may be indicative of more stringent and less dynamic brain function in AD patients. Our findings provided insightful implications for understanding the pathophysiological mechanisms of brain dysfunctions in aMCI and AD patients and demonstrated that functional disorders can be characterized by multimodal neuroimaging-based metrics.

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Figure 1 The flowchart of the analysis steps. (A) The construction of resting-state functional connectivity (FC) network for graph theoretic analysis. Note that, two parcellation scales (low: 90 ROIs, and high: 1024 ROIs) were employed to estimate the graph metrics. For better presentation purpose, only low parcellation scale was utilized here. (B) The construction of structural connectivity (SC) network for assessing the FC-SC relationship. In the current study, the level of coupling between FC and SC was examined by calculating the level of correlation between the weights of existing SC and their functional counterparts.
Figure 1 The flowchart of the analysis steps. (A) The construction of resting-state functional connectivity (FC) network for graph theoretic analysis. Note that, two parcellation scales (low: 90 ROIs, and high: 1024 ROIs) were employed to estimate the graph metrics. For better presentation purpose, only low parcellation scale was utilized here. (B) The construction of structural connectivity (SC) network for assessing the FC-SC relationship. In the current study, the level of coupling between FC and SC was examined by calculating the level of correlation between the weights of existing SC and their functional counterparts.CC BY · PubMed Central
Figure 2 Small-world network metrics of functional brain networks at two different parcellation scales. The graphs show the changes in γ (solid line) and λ (dashed line) in the functional brain networks of AD (red), aMCI (green), and NC (blue) groups at (A) low parcellation scale and (B) high parcellation scale with respect to sparsity. For all the three groups, γ was significantly greater than 1 and λ was relatively close to 1, indicating prominent small-world properties for all groups.
Figure 2 Small-world network metrics of functional brain networks at two different parcellation scales. The graphs show the changes in γ (solid line) and λ (dashed line) in the functional brain networks of AD (red), aMCI (green), and NC (blue) groups at (A) low parcellation scale and (B) high parcellation scale with respect to sparsity. For all the three groups, γ was significantly greater than 1 and λ was relatively close to 1, indicating prominent small-world properties for all groups.CC BY · PubMed Central
Figure 3 The distribution of brain regions with both significant (p<0.05, uncorrected) groups effects in BC and significant (p<0.05) correlation between BC and MMSE scores. Each bar represents the average BC values for AD (red), aMCI (green), and NC (blue). Error bars represent standard deviations (S.D.). The regions were overlaid on the brain surface at the Medium view. Post hoc pairwise comparison was performed through Fisher’s least significant difference (LSD) with * indicates p<0.05 and ** indicates p<0.01. Regions showing positive correlation are coded in purple and region showing negative correlation is coded in light grey. For the abbreviations of cortical regions, see Table S1. L = left; R = right. The figure was visualized with BrainNet Viewer software (http://www.nitrc.org/projects/bnv/).
Figure 3 The distribution of brain regions with both significant (p<0.05, uncorrected) groups effects in BC and significant (p<0.05) correlation between BC and MMSE scores. Each bar represents the average BC values for AD (red), aMCI (green), and NC (blue). Error bars represent standard deviations (S.D.). The regions were overlaid on the brain surface at the Medium view. Post hoc pairwise comparison was performed through Fisher’s least significant difference (LSD) with * indicates p<0.05 and ** indicates p<0.01. Regions showing positive correlation are coded in purple and region showing negative correlation is coded in light grey. For the abbreviations of cortical regions, see Table S1. L = left; R = right. The figure was visualized with BrainNet Viewer software (http://www.nitrc.org/projects/bnv/).CC BY · PubMed Central
Figure 4 Modularity Q of the functional brain networks for AD, aMCI and NC groups with respect to sparsity. The black dashed line indicates the threshold for the nonrandom community structure. Note that only the modularity of AD group is less than 0.3 for S = 25%.
Figure 4 Modularity Q of the functional brain networks for AD, aMCI and NC groups with respect to sparsity. The black dashed line indicates the threshold for the nonrandom community structure. Note that only the modularity of AD group is less than 0.3 for S = 25%.CC BY · PubMed Central
Figure 5 The modular structure of group averaged functional brain network at S = 10%. (A) Color coded modular structure plotted in the anatomical space. The brain network is presented with the frontal cortex on the left of the panel and the occipital region on the right of the panel. Inter-modular links are colored in gray for better illustration. Detailed information about the module regions and regional node roles for AD, aMCI and NC groups are presented in (B). The module order was determined according the descending order of the region numbers in AD group. The topological representations were drawn by the Pajek (http://pajek.imfm.si/doku.php). For the abbreviations of cortical regions, see Table S1. L = left; R = right.
Figure 5 The modular structure of group averaged functional brain network at S = 10%. (A) Color coded modular structure plotted in the anatomical space. The brain network is presented with the frontal cortex on the left of the panel and the occipital region on the right of the panel. Inter-modular links are colored in gray for better illustration. Detailed information about the module regions and regional node roles for AD, aMCI and NC groups are presented in (B). The module order was determined according the descending order of the region numbers in AD group. The topological representations were drawn by the Pajek (http://pajek.imfm.si/doku.php). For the abbreviations of cortical regions, see Table S1. L = left; R = right.CC BY · PubMed Central
Figure 6 The overall FC-SC relationships. (A) The probability densities of FC strengths between structurally connected and unconnected regions pairs. When direct SC presents, higher FC strength appears than the case direct SC absents, indicating a robust relationship between FC and SC. (B) Scatter plot of SC strength against FC strength for the regions with direct SC. Further analysis showed significant coefficient between FC and SC strengths for all groups. Data are from group averaged functional connectivity matrix of AD, aMCI, and NC groups, at the high resolution (1024×1024).
Figure 6 The overall FC-SC relationships. (A) The probability densities of FC strengths between structurally connected and unconnected regions pairs. When direct SC presents, higher FC strength appears than the case direct SC absents, indicating a robust relationship between FC and SC. (B) Scatter plot of SC strength against FC strength for the regions with direct SC. Further analysis showed significant coefficient between FC and SC strengths for all groups. Data are from group averaged functional connectivity matrix of AD, aMCI, and NC groups, at the high resolution (1024×1024).CC BY · PubMed Central
Figure 7 The functional connectivity (FC) – structural connectivity (SC) coupling for AD patients, aMCI patients, and healthy controls. AD patients exhibited a significant decrease in FC-SC coupling compared to aMCI patients and healthy controls. Significant statistical differences are indicated with * for p<0.05 and ** for p<0.01.
Figure 7 The functional connectivity (FC) – structural connectivity (SC) coupling for AD patients, aMCI patients, and healthy controls. AD patients exhibited a significant decrease in FC-SC coupling compared to aMCI patients and healthy controls. Significant statistical differences are indicated with * for p<0.05 and ** for p<0.01.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Q Guo, 2009, A comparison study of mild cognitive impairment with 3 memory tests among Chinese individuals, Alzheimer Dis Assoc Disord, 23, 253, 10.1097/WAD.0b013e3181999e92 · DOI

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Metadata

Authors
Sun, Y., Yin, Q., Fang, R., Yan, X., Wang, Y., Bezerianos, A., Tang, H., Miao, F., & Sun, J.
Affiliations
  • National University of Singapore (SG)
  • Shanghai Jiao Tong University (CN)
Corresponding author
  • Tang, Huidong
  • Miao, Fei
  • Sun, Junfeng
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2014-05-07
Volume
9
Issue
5
Pages
e96505
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0096505
PMC
PMC4013022
OpenAlex
W1998486151
Cited by
122
References
83
Citations by year
2026: 4 · 2025: 11 · 2024: 11 · 2023: 6 · 2022: 8 · 2021: 9 · 2020: 12 · 2019: 10 · 2018: 14 · 2017: 11 · 2016: 11 · 2015: 12 · 2014: 3
FWCI
6.88
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • National University of Singapore (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • Shanghai Jiao Tong University (YG2012MS09)
  • National Key Research and Development Program of China (2011CB013304)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
Alzheimer's disease, amnestic mild cognitive impairment, functional connectivity, structural connectivity, small-world networks, brain network topology, modular organization, healthy aging, dementia continuum, diffusion tensor tractography
MeSH terms
Aged, Aged, 80 and over, Aging, Alzheimer Disease, Amnesia, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neuropsychological Tests, Disease Progression, Diffusion Tensor Imaging, Neuroimaging, Cognitive Dysfunction

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